Urgency level estimation apparatus, urgency level estimation method, and program

ABSTRACT

An urgency level estimation technique of estimating an urgency level of a speaker for free uttered speech, which does not require a specific word, is provided. An urgency level estimation apparatus includes a feature amount extracting part configured to extract a feature amount of an utterance from uttered speech, and an urgency level estimating part configured to estimate an urgency level of a speaker of the uttered speech from the feature amount based on a relationship between a feature amount extracted from uttered speech and an urgency level of a speaker of the uttered speech, the relationship being determined in advance, and the feature amount includes at least one of a feature indicating speaking speed of the uttered speech, a feature indicating voice pitch of the uttered speech and a feature indicating a power level of the uttered speech.

TECHNICAL FIELD

The present invention relates to a technique for estimating an urgencylevel of a call from uttered speech.

BACKGROUND ART

When an urgency level of a call can be estimated from speech left on ananswering machine, it becomes possible to select a call that should bepreferentially handled.

In conventional call urgency level estimation, whether a call is urgentor non-urgent has been estimated from a vocal tract feature amount suchas MFCC (Mel-Frequency Cepstral Coefficients) and PNCC (Power NormalizedCepstral Coefficients), for example, for specific words such as “Help”(Non-patent literature 1).

PRIOR ART LITERATURE Non-Patent Literature

-   Non-patent literature 1: E. Principi, S. Squartini, E. Cambria, F.    Piazza, “Acoustic template-matching for automatic emergency state    detection: An ELM based algorithm”, Neurocomputing, Vol. 149, Part    A, pp. 426-434, 2015.

SUMMARY OF THE INVENTION Problems to be Solved by the Invention

However, because, in Non-patent literature 1, a vocal tract featureamount of a specific word is used, there is a problem that the urgencylevel cannot be estimated from speech not including the word.

Therefore, the present invention is directed to providing an urgencylevel estimation technique for estimating an urgency level of a speakerfor free uttered speech, which does not require a specific word.

Means to Solve the Problems

One aspect of the present invention is an urgency level estimationapparatus comprising a feature amount extracting part configured toextract a feature amount of an utterance from uttered speech, and anurgency level estimating part configured to estimate an urgency level ofa speaker of the uttered speech from the feature amount based on arelationship between a feature amount extracted from uttered speech andan urgency level of a speaker of the uttered speech, the relationshipbeing determined in advance, in which the feature amount includes atleast one of a feature indicating speaking speed of the uttered speech,and a feature indicating voice pitch of the uttered speech, and afeature indicating a power level of the uttered speech.

One aspect of the present invention is an urgency level estimationapparatus comprising a vocal tract feature amount extracting partconfigured to extract a vocal tract feature amount for each frameobtained by dividing uttered speech, from the uttered speech, a vocaltract feature amount statistical value calculating part configured tocalculate a mean and a variance value from the vocal tract featureamount as vocal tract feature amount statistical values of the utteredspeech, a speech recognizing part configured to generate a set ofreading, an utterance start time and an utterance end time for eachutterance section included in the uttered speech, from the utteredspeech, a first speaking speed estimating part configured to estimatespeaking speed of the uttered speech from the set of the reading, theutterance start time and the utterance end time, and an urgency levelestimating part configured to estimate an urgency level of a speaker ofthe uttered speech from the mean, the variance value, and the speakingspeed using an urgency level estimation model learned so that a mean anda variance value of a vocal tract feature amount of uttered speech andspeaking speed of the uttered speech are input, and an urgency level ofa speaker of the uttered speech is output.

One aspect of the present invention is an urgency level estimationapparatus comprising a vocal tract feature amount extracting partconfigured to extract a vocal tract feature amount for each frameobtained by dividing uttered speech, from the uttered speech, a vocaltract feature amount statistical value calculating part configured tocalculate a mean and a variance value from the vocal tract featureamount as vocal tract feature amount statistical values of the utteredspeech, an F0 information extracting part configured to extract F0information for each frame obtained by dividing the uttered speech, fromthe uttered speech, an F0 information statistical value calculating partconfigured to calculate a difference between an average value and amedian value of the F0 information, from the F0 information, and anurgency level estimating part configured to estimate an urgency level ofa speaker of the uttered speech from the mean, the variance value andthe difference using an urgency level estimation model learned so that amean and a variance value of a vocal tract feature amount of utteredspeech, and a difference between an average value and a median value ofF0 information of the uttered speech are input, and an urgency level ofa speaker of the uttered speech is output.

One aspect of the present invention is an urgency level estimationapparatus comprising a vocal tract feature amount extracting partconfigured to extract a vocal tract feature amount for each frameobtained by dividing uttered speech, from the uttered speech, a vocaltract feature amount statistical value calculating part configured tocalculate a mean and a variance value from the vocal tract featureamount as vocal tract feature amount statistical values of the utteredspeech, an F0 information extracting part configured to extract F0information for each frame obtained by dividing the uttered speech, fromthe uttered speech, a power extracting part configured to extract powerfor each frame obtained by dividing the uttered speech, from the utteredspeech, a power average adjusting part configured to calculate adjustedpower adjusted using power average from the F0 information and thepower, a power maximum value calculating part configured to calculate apower maximum value which is a maximum value of the adjusted power, fromthe adjusted power, and an urgency level estimating part configured toestimate an urgency level of a speaker of the uttered speech from themean, the variance value and the power maximum value using an urgencylevel estimation model learned so that a mean and a variance value of avocal tract feature amount of uttered speech, and a maximum value ofadjusted power of the uttered speech are input, and an urgency level ofa speaker of the uttered speech is output.

One aspect of the present invention is an urgency level estimationapparatus comprising a vocal tract feature amount extracting partconfigured to extract a vocal tract feature amount for each frameobtained by dividing uttered speech, from the uttered speech, a vocaltract feature amount statistical value calculating part configured tocalculate a mean and a variance value from the vocal tract featureamount as vocal tract feature amount statistical values of the utteredspeech, a posterior probability sequence estimating part configured toestimate a posterior probability sequence indicating a probability ofsound corresponding to a frame obtained by dividing the uttered speech,the sound being each phoneme, from the tittered speech, using a speechrecognition acoustic model for identifying a phoneme from input sound, asecond speaking speed estimating part configured to estimate speakingspeed of the uttered speech from the posterior probability sequenceusing a speaking speed estimation model learned so that a posteriorprobability sequence of uttered speech is input, and speaking speed ofthe uttered speech is output, and an urgency level estimating partconfigured to estimate an urgency level of a speaker of the utteredspeech from the mean, the variance value and the speaking speed using anurgency level estimation model learned so that a mean and a variancevalue of a vocal tract feature amount of uttered speech, and speakingspeed of the uttered speech are input, and an urgency level of a speakerof the uttered speech is output.

Effects of the Invention

According to the present invention, it becomes possible to estimate anurgency level of a speaker for free uttered speech, which does notrequire a specific word.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a view illustrating an example of a difference in speakingspeed (seconds per mora) between urgent speech and non-urgent speech;

FIG. 2 is a block diagram illustrating an example of a configuration ofan urgency level estimation apparatus 100;

FIG. 3 is a flowchart illustrating an example of operation of theurgency level estimation apparatus 100;

FIG. 4 is a view illustrating an example of a vocal tract featureamount;

FIG. 5 is a view illustrating an example of a speech recognition result;

FIG. 6 is a view illustrating an example of average values, medianvalues, and differences between the average values and the median valuesof F0 of urgent speech and non-urgent speech;

FIG. 7 is a block diagram illustrating an example of a configuration ofan urgency level estimation apparatus 200;

FIG. 8 is a flowchart illustrating an example of operation of theurgency level estimation apparatus 200;

FIG. 9 is a view illustrating an example of F0 information;

FIG. 10A is a view illustrating an example of change of power of urgentspeech;

FIG. 10B is a view illustrating an example of change of power ofnon-urgent speech;

FIG. 11 is a block diagram illustrating an example of a configuration ofan urgency level estimation apparatus 300;

FIG. 12 is a flowchart illustrating an example of operation of theurgency level estimation apparatus 300;

FIG. 13 is a view illustrating an example of a posterior probabilitysequence;

FIG. 14 is a block diagram illustrating an example of a configuration ofan urgency level estimation apparatus 400;

FIG. 15 is a flowchart illustrating an example of operation of theurgency level estimation apparatus 400;

FIG. 16 is a block diagram illustrating an example of a configuration ofan urgency level estimation apparatus 500;

FIG. 17 is a flowchart illustrating an example of operation of theurgency level estimation apparatus 500;

FIG. 18 is a block diagram illustrating an example of a configuration ofan urgency level estimation apparatus 501;

FIG. 19 is a flowchart illustrating an example of operation of theurgency level estimation apparatus 501;

FIG. 20 is a block diagram illustrating an example of a configuration ofan urgency level estimation apparatus 502; and

FIG. 21 is a flowchart illustrating an example of operation of theurgency level estimation apparatus 502.

DETAILED DESCRIPTION OF THE EMBODIMENTS

Embodiments of the present invention will be described in detail below.Note that the same numbers will be assigned to components having thesame functions, and overlapped description will be omitted.

First Embodiment

FIG. 1 illustrate a result of analyzing speaking speed (hereinafter,referred to as speaking speed) in urgent/non-urgent speech using aplurality of pieces of voicemail simulated speech. From this drawing, itcan be seen that the speaking speed, that is, the number of seconds permora (phonological segment) is smaller in the urgent speech. Therefore,in the first embodiment, the urgency level is estimated using thespeaking speed. Note that a statistical value of a vocal tract featureamount used conventionally is also used along with the speaking speed inurgency level estimation.

An urgency level estimation apparatus 100 will be described below withreference to FIGS. 2 to 3. FIG. 2 is a block diagram illustrating aconfiguration of the urgency level estimation apparatus 100. FIG. 3 is aflowchart illustrating operation of the urgency level estimationapparatus 100. As illustrated in FIG. 2, the urgency level estimationapparatus 100 includes a vocal tract feature amount extracting part 110,a vocal tract feature amount statistical value calculating part 120, aspeech recognizing part 130, a first speaking speed estimating part 140,an urgency level estimating part 150, and a recording part 190. Therecording part 190 is a component which records information necessaryfor processing of the urgency level estimation apparatus 100 asappropriate.

The urgency level estimation apparatus 100 reads an urgency levelestimation model 180 and executes processing. The urgency levelestimation model 180 may be configured to be recorded in an externalrecording part as illustrated in FIG. 2 or may be configured to berecorded in the recording part 190.

The urgency level estimation apparatus 100 estimates an urgency level ofa speaker of uttered speech s(t) from uttered speech s(t) (t=0, 1, 2, .. . , T, t represents a sample number) and outputs the urgency level.The uttered speech s(t) is a speech signal sampled every unit time, and,for example, speech of a message left on an answering machine.

Operation of the urgency level estimation apparatus 100 will bedescribed with reference to FIG. 3. The vocal tract feature amountextracting part 110 receives the uttered speech s(t) (t=0, 1, 2, . . . ,T) as input, and extracts and outputs a vocal tract feature amount c(i)(i=0, 1, 2, . . . , I, i represents a frame number) for each frameobtained by dividing the uttered speech s(t) (S110). As the vocal tractfeature amount, for example, MFCC or cepstrum can be used. Further, anymethod may be used for the extraction. The vocal tract feature amountc(i) can be obtained as a vector sequence corresponding to a time lengthof the speech as illustrated in, for example, FIG. 4. In FIG. 4, thelength of the sequence is the total number of frames I.

The vocal tract feature amount statistical value calculating part 120calculates a mean mean(c) and a variance value var(c) as vocal tractfeature amount statistical values of the uttered speech s(t) from thevocal tract feature amount c(i) (i=0, 1, 2, . . . , I) extracted inS110, and outputs the mean mean(c) and the variance value var(c) (S120).The mean mean(c) and the variance value var(c) can be respectivelycalculated using the following formulas.

$\begin{matrix}{{{{mean}\; (c)} = {\frac{1}{I}{\sum_{i = 0}^{I}{c(i)}}}}{{{var}(c)} = {\frac{1}{I}{\sum_{i = 0}^{I}\left( {{c(i)} - {{mean}(c)}} \right)^{2}}}}} & \left\lbrack {{Formula}\mspace{14mu} 1} \right\rbrack\end{matrix}$

The speech recognizing part 130 generates a speech recognition resultW(j) (j=1, 2, . . . , J, j represents an utterance section number) foreach utterance section included in the uttered speech s(t) from theuttered speech s(t) (t=0, 1, 2, . . . , T) and outputs the speechrecognition result W(j) (S130). Here, the speech recognition result W(j)(j=1, 2, . . . , J) is the speech recognition result of the utterancesection detected from the uttered speech s(t) using a predeterminedmethod. While any method may be used for the speech recognition, insteadof text in which kanji and kana are mixed which is normally generated asthe speech recognition result, a set of reading w(j) of the utterancesection j, utterance start time s(j) and utterance end time e(j),corresponding to additional information is set as the speech recognitionresult W(j) (see FIG. 5). The reading w(j) is a sentence that describespronounced sound in kana. Further, the utterance start time s(j) and theutterance end time e(j) are values respectively indicating start time(second) and end time (second) of the speech signal in the utterancesection j. Note that the utterance start time and the utterance end timeof each utterance section are displayed assuming that the start time ofthe uttered speech s(t) is 0 seconds. In other words, the speechrecognizing part 130 generates a set of the reading w(j), the utterancestart time s(j) and the utterance end time e(j) (j=1, 2, . . . , J) foreach utterance section included in the uttered speech s(t) from theuttered speech s(t) (t=0, 1, 2, . . . , T) and outputs the set (S130).

The first speaking speed estimating part 140 estimates the speakingspeed mean(r) of the uttered speech s(t) from the speech recognitionresult W(j) generated in S130 (that is, a set of the reading w(j), thespeech start time s(j), and the speech end time e(j)) (j=1, 2, . . . ,J), and outputs the speaking speed mean(r) (S140). In estimation of thespeaking speed mean(r), mean(r) [seconds/mora] is obtained from anutterance period and the number of mora assuming the number ofcharacters obtained by excluding contracted sound (“ya”, “yu”, “yo”)included in the reading w(j) as the number of mora. When the number ofcharacters excluding contracted sound included in the reading w(j) isset as len(w(j)), the speaking speed mean(r) can be obtained by thefollowing formula.

$\begin{matrix}{{{mean}(r)} = \frac{\sum_{j = 0}^{J}\left( {{e(j)} - {s(j)}} \right)}{\sum_{j = 0}^{J}{{len}\left( {w(j)} \right)}}} & \left\lbrack {{Formula}\mspace{14mu} 2} \right\rbrack\end{matrix}$

Here, e(j)-s(j) is a period (utterance period) required to utter thereading w(j).

From this formula, it can be seen that the speaking speed mean(r) isaverage speaking speed of the uttered speech.

The urgency level estimating part 150 estimates an urgency level of aspeaker of the uttered speech s(t) (t=0, 1, 2, . . . , T) from the meanmean(c), the variance value var(c) calculated in S120 and the speakingspeed mean(r) estimated in S140 using the urgency level estimation model180 and outputs the urgency level (S150). In the urgency levelestimation model 180, a mean and a variance value of a vocal tractfeature amount of uttered speech and speaking speed of the utteredspeech are input, and an urgency level of a speaker of the utteredspeech is output.

The urgency level estimation model is generated by machine learning suchas, for example, a support vector machine (SVM), a random forest, and aneural network. Specifically, first, sets each including feature amounts(here, the mean mean(c), the variance value var(c), the speaking speedmean(r)) obtained from a speech signal whose urgency level is known inadvance, and information (correct answer label) indicating the urgencylevel of the speech signal are prepared as learning data. Next, theurgency level is estimated using this learning data, using the featureamounts as input, and using the urgency level estimation model, andparameters of the urgency level estimation model are updated so as tomake an error between the urgency level which is an estimation resultand an urgency level of the correct answer label smaller. Note thatappropriate initial values are given as the parameters of the urgencylevel estimation model upon start of learning of the urgency levelestimation model. Then, updating (that is, learning) of the parametersis finished when predetermined conditions are satisfied. Note that thecorrect answer label may have two stages of urgency and non-urgency, ormay have three or more stages by dividing the urgency level into threeor more ranks.

According to the present invention, it becomes possible to estimate anurgency level of a speaker for free uttered speech, which does notrequire a specific word.

Second Embodiment

FIG. 6 illustrates a result of analyzing voice pitch (F0) ofurgent/non-urgent speech using a plurality of pieces of voicemailsimulated speech. From this drawing, it can be seen that voice tends tobe higher on average in case of urgency. Therefore, in the secondembodiment, the urgency level is estimated using the voice pitch of thespeech. Note that the statistical values of the vocal tract featureamount are also used in a similar manner to the first embodiment.

Note that it is difficult to estimate the urgency/non-urgency only froman average value of F0, because the average pitch of the voice variesdepending on genders and individuals. Therefore, here, a difference fromthe average value and a median value of F0 is used as the feature amountfor estimation by utilizing characteristics that the median value of F0in urgent/non-urgent speech does not change significantly. By this meansit becomes possible to estimate the urgency level while absorbingindividuality of F0.

An urgency level estimation apparatus 200 will be described below withreference to FIGS. 7 to 8. FIG. 7 is a block diagram illustrating aconfiguration of the urgency level estimation apparatus 200. FIG. 8 is aflowchart illustrating operation of the urgency level estimationapparatus 200. As illustrated in FIG. 7, the urgency level estimationapparatus 200 includes the vocal tract feature amount extracting part110, the vocal tract feature amount statistical value calculating part120, an F0 information extracting part 210, an F0 informationstatistical value calculating part 220, and an urgency level estimatingpart 250, and a recording part 290. The recording part 290 is acomponent which records information necessary for processing of theurgency level estimation apparatus 200 as appropriate.

The urgency level estimation apparatus 200 reads an urgency levelestimation model 280 and executes processing. The urgency levelestimation model 280 may be configured to be recorded in an externalrecording part as illustrated in FIG. 7 or may be configured to berecorded in the recording part 290.

The urgency level estimation apparatus 200 estimates the urgency levelof a speaker of the uttered speech s(t) from the uttered speech s(t)(t=0, 1, 2, . . . , T, t represents a sample number) and outputs theurgency level.

Operation of the urgency level estimation apparatus 200 will bedescribed with reference to FIG. 8. The vocal tract feature amountextracting part 110 receives the uttered speech s(t) (t=0, 1, 2, . . . ,T) as input, and extracts and outputs a vocal tract feature amount c(i)(i=0, 1, 2, . . . , I, i represents a frame number) for each frameobtained by dividing the uttered speech s(t) (S110). The vocal tractfeature amount statistical value calculating part 120 calculates a meanmean(c) and a variance value var(c) as vocal tract feature amountstatistical values of the uttered speech s(t) from the vocal tractfeature amount c(i) (i=0, 1, 2, . . . , I) extracted in S110, andoutputs the mean mean(c) and the variance value var(c) (S120).

The F0 information extracting part 210 receives the uttered speech s(t)(t=0, 1, 2, . . . , T) as input, and extracts and outputs F0 informationf(k) (k=0, 1, 2, . . . , K, k represents a frame number) for each frameobtained by dividing the uttered speech s(t) (S210). The F0 informationf(k) is a feature amount of the voice pitch in a frame k. Any method maybe used to extract the F0 information. FIG. 9 illustrates an example ofthe extracted F0 information.

The F0 information statistical value calculating part 220 calculates adifference medave(f) between the average value and the median value ofthe F0 information from the F0 information f(k) (k=0, 1, 2, . . . , K)extracted in S210 and outputs the difference medave(f) (S220). As can beseen from FIG. 9, the value of F0 is 0 in a section which is not avoiced section (unvoiced or silent section). If the average value or themedian value is obtained in a section including a section in which thevalue of F0 is 0, because features other than the features of theuttered speech are also included, the average ave(f) and the medianvalue med(f) of the F0 information are obtained using only the voicedsection. Specifically, the average value and the median value areobtained as follows. First, a voiced/unvoiced determination resultv(f(k)) indicating whether or not the frame k is a voiced section isdefined by the following formula.

$\begin{matrix}{{v\left( {f(k)} \right)} = \left\{ \begin{matrix}0 & {{f(k)} = 0} \\1 & {{f(k)} > 0}\end{matrix} \right.} & \left\lbrack {{Formula}\mspace{14mu} 3} \right\rbrack\end{matrix}$

Next, the median value med(f) of the F0 information is calculated as anaverage value of a minimum value min(f) of the F0 information and amaximum value max(f) of the F0 information.

$\begin{matrix}{{{\min (f)} = {\min\limits_{k \in {\{{{k|{v{({f{(k)}})}}} = 1}\}}}{f(k)}}}{{\max (f)} = {\max\limits_{k \in {\{{{k|{v{({f{(k)}})}}} = 1}\}}}{f(k)}}}{{{med}(f)} = \frac{{\max (f)} + {\min (f)}}{2}}} & \left\lbrack {{Formula}\mspace{14mu} 4} \right\rbrack\end{matrix}$

Further, the average value ave(f) of the F0 information is calculated bythe following formula.

$\begin{matrix}{{av{e(f)}} = \frac{\sum_{k = 0}^{K}{f(k)}}{\sum_{k = 0}^{K}{v\left( {f(k)} \right)}}} & \left\lbrack {{Formula}\mspace{14mu} 5} \right\rbrack\end{matrix}$

Then, the difference medave(f) is calculated by the following formula.

medave(f)=ave(f)−med(f)  [Formula 6]

The urgency level estimating part 250 estimates the urgency level of aspeaker of the uttered speech s(t) (t=0, 1, 2, . . . , T) from the meanmean(c) and the variance value var(c) calculated in S120, and thedifference medave (f) calculated in S220 using the urgency levelestimation model 280 and outputs the urgency level of a speaker (S250).In the urgency level estimation model 280, a mean and a variance valueof a vocal tract feature amount of uttered speech and a differencebetween an average value and a median value of F0 information of theuttered speech are input, and an urgency level of a speaker of theuttered speech is output. The learning method of the urgency levelestimation model 280 may be similar to that in the first embodiment.

According to the present invention, it becomes possible to estimate anurgency level of a speaker for free uttered speech, which does notrequire a specific word.

Third Embodiment

While power of the uttered speech varies depending on telephone devices,as can be seen from FIGS. 10A and 10B, when the average power of oneutterance is aligned and speech with a high urgency level is comparedwith speech with a low urgency level, voice in the speech with a highurgency level tends to be partially strong. Therefore, in the thirdembodiment, the urgency level is estimated by using strength of thevoice while the average power is aligned and a maximum value of thepower is set as the strength of the voice. Note that the statisticalvalues of the vocal tract feature am omit are also used in a similarmanner to the first embodiment.

An urgency level estimation apparatus 300 will be described below withreference to FIGS. 11 to 12. FIG. 11 is a block diagram illustrating aconfiguration of the urgency level estimation apparatus 300. FIG. 12 isa flowchart illustrating operation of the urgency level estimationapparatus 300. As illustrated in FIG. 11, the urgency level estimationapparatus 300 includes the vocal tract feature amount extracting part110, the vocal tract feature amount statistical value calculating part120, the F0 information extracting part 210, a power extracting part310, a power average adjusting part 320, and a power maximum valuecalculating part 330, an urgency level estimating part 350, and arecording part 390. The recording part 390 is a component which recordsinformation necessary for processing of the urgency level estimationapparatus 300 as appropriate.

The urgency level estimation apparatus 300 reads an urgency levelestimation model 380 and executes processing. Note that the urgencylevel estimation model 380 may be configured to be recorded in anexternal recording part as illustrated in FIG. 11 or may be configuredto be recorded in the recording part 390.

The urgency level estimation apparatus 300 estimates the urgency levelof a speaker of the uttered speech s(t) from the uttered speech s(t)(t=0, 1, 2, . . . , T, t represents a sample number) and outputs theurgency level of a speaker.

Operation of the urgency level estimation apparatus 300 will bedescribed with reference to FIG. 12. The vocal tract feature amountextracting part 110 receives the uttered speech s(t) (t=0, 1, 2, . . . ,T) as input, and extracts and outputs a vocal tract feature amount c(i)(i=0, 1, 2, . . . , I, i represents a frame number) for each frameobtained by dividing the uttered speech s(t) (S110). The vocal tractfeature amount statistical value calculating part 120 calculates a meanmean(c) and a variance value var(c) as vocal tract feature amountstatistical values of the uttered speech s(t) from the vocal tractfeature amount c(i) (i=0, 1, 2, . . . , I) extracted in S110, andoutputs the mean mean(c) and the variance value var(c) (S120). The F0information extracting part 210 receives the uttered speech s(t) (t=0,1, 2, . . . , T) as input, and extracts and outputs F0 information f(k)(k=0, 1, 2, . . . , K, k represents a frame number) for each frameobtained by dividing the uttered speech s(t) (S210).

The power extracting part 310 receives the uttered speech s(t) (t=0, 1,2, . . . , T) as input, and extracts and outputs power p(k) (k=(0, 1, 2,. . . , K, k represents a frame number) for each frame obtained bydividing the uttered speech s(t) (S310). The power p(k) is a featureamount of strength of the voice in the frame k. Any method may be usedfor the power extraction.

The power average adjusting part 320 calculates adjusted power p′(k)(k=0, 1, 2, . . . , K) from the F0 information f(k) (k=0, 1, 2, . . . ,K) extracted in S210 and the power p(k) (k=0, 1, 2, . . . , K) extractedin S310 using the power average and outputs the adjusted power p′(k)(S320). The uttered speech includes a silent section (that is, a sectionin which no speech is contained). Therefore, if the power average iscalculated in a section including the silent section, the calculatedpower average is likely to be small in a frame k with many silentsections, so that there is a case where the calculated adjusted powerp′(k) is large. Therefore, by dividing the power by the power averagefor a voiced section (frame where f(k)>0) where there can be certainlyspeech to align the power, it is possible to obtain power (that is,adjusted power) which absorbs a difference in a recording level of thetelephone regardless of a time length of the silent section.Specifically, the power is obtained as follows. First, a voiced/unvoiceddetermination result v(f(k)) indicating whether or not the frame k is avoiced section is defined by the following formula.

$\begin{matrix}{{v\left( {f(k)} \right)} = \left\{ \begin{matrix}0 & {{f(k)} = 0} \\1 & {{f(k)} > 0}\end{matrix} \right.} & \left\lbrack {{Formula}\mspace{14mu} 7} \right\rbrack\end{matrix}$

Then, the adjusted power p′(k) is calculated using the followingformula.

$\begin{matrix}{{{p^{\prime}(k)} = \frac{p(k)}{{mean}(p)}}{{{mean}(p)} = \frac{\sum_{k = 0}^{K}{{v\left( {f(k)} \right)}*{p(k)}}}{\sum_{k = 0}^{K}{v\left( {f(k)} \right)}}}} & \left\lbrack {{Formula}\mspace{14mu} 8} \right\rbrack\end{matrix}$

The power maximum value calculating part 330 calculates a power maximumvalue max(p) which is a maximum value of the adjusted power from theadjusted power p′(k) (k=0, 1, 2, . . . , K) calculated in S320 andoutputs the power maximum value max(p) (S330). The power maximum valuemax(p) can be obtained using the following formula.

$\begin{matrix}{{\max (p)} = {\max\limits_{k}\; {p^{\prime}(k)}}} & \left\lbrack {{Formula}\mspace{14mu} 9} \right\rbrack\end{matrix}$

The urgency level estimating part 350 estimates the urgency level of aspeaker s(t) (t=0, 1, 2, . . . , T) from the mean mean(c) and thevariance value var(c) calculated in S120 and the power maximum valuemax(p) calculated in S330 using the urgency level estimation model 380and outputs the urgency level of a speaker of the uttered speech s(t)(S350). In the urgency level estimation model 380, a mean and a variancevalue of a vocal tract feature amount of uttered speech and a maximumvalue of adjusted power of the uttered speech are input, and an urgencylevel of a speaker of the uttered speech is output. A learning method ofthe urgency level estimation model 380 may be similar to that in thefirst embodiment.

According to the present invention, it becomes possible to estimate anurgency level of a speaker for free uttered speech, which does notrequire a specific word.

Fourth Embodiment

While, in the first embodiment, the speaking speed is estimated usingspeech recognition, if a word used in the uttered speech is not includedin a model to be used for speech recognition, because an accurate speechrecognition result cannot be obtained, the speaking speed cannot becorrectly estimated. Therefore, tuning work for registering words in themodel to be used for speech recognition is indispensable. However, it isnot realistic to register all words in advance for utterances withoutrestrictions, such as messages left on the answering machine. Therefore,in the fourth embodiment, the urgency level is estimated using speakingspeed estimated from change in posterior probability (a posterioriprobability sequence) of an acoustic model for speech recognition (amodel for identifying which phoneme the input sound is). Note that thestatistical value of the vocal tract feature amount is also used in asimilar manner to the first embodiment.

FIG. 13 illustrates an example of the posterior probability sequence. Inthe table in FIG. 13, a horizontal direction is phoneme information, avertical direction is time (frame number), and a value in each field isa value indicating how much degree of probability a phonemecorresponding to the sound in each frame is correct. When the speakingspeed is fast, transition of the posterior probability is fast, while,when the speaking speed is slow, the transition of the posteriorprobability is slow. By capturing characteristics of the speed of thetransition and obtaining the speaking speed approximately, it ispossible to estimate the speaking speed even if an accurate speechrecognition result cannot be obtained.

An urgency level estimation apparatus 400 will be described below withreference to FIGS. 14 to 15. FIG. 14 is a block diagram illustrating aconfiguration of the urgency level estimation apparatus 400. FIG. 15 isa flowchart illustrating operation of the urgency level estimationapparatus 400. As illustrated in FIG. 14, the urgency level estimationapparatus 400 includes the vocal tract feature amount extracting part110, the vocal tract feature amount statistical value calculating part120, a posterior probability sequence estimating part 410, a secondspeaking speed estimating part 420, and an urgency level estimating part450 and a recording part 490. The recording part 490 is a componentwhich records information necessary for processing of the urgency levelestimation apparatus 400 as appropriate.

The urgency level estimation apparatus 400 reads an urgency levelestimation model 480, a speech recognition acoustic model 482, and aspeaking speed estimation model 484, and executes processing. Note thatthe urgency level estimation model 480, the speech recognition acousticmodel 482, and the speaking speed estimation model 484 may be configuredto be recorded in an external recording part as illustrated in FIG. 14or may be configured to be recorded in the recording part 490.

The urgency level estimation apparatus 400 estimates the urgency levelof a speaker of the uttered speech s(t) from the uttered speech s(t)(t=0, 1, 2, . . . , T, t represents a sample number) and outputs theurgency level of a speaker of the uttered speech s(t).

Operation of the urgency level estimation apparatus 400 will bedescribed with reference to FIG. 15. The vocal tract feature amountextracting part 110 receives the uttered speech s(t) (t=0, 1, 2, . . . ,T) as input, and extracts and outputs a vocal tract feature amount c(i)(i=0, 1, 2, . . . , I, i represents a frame number) for each frameobtained by dividing the uttered speech s(t) (S110). The vocal tractfeature amount statistical value calculating part 120 calculates a meanmean(c) and a variance value var(c) as a vocal tract feature amountstatistical value of the uttered speech s(t) from the vocal tractfeature amount c(i) (i=0, 1, 2, . . . , I) extracted in S110, andoutputs the mean mean(c) and the variance value var(c) (S120).

The posterior probability sequence estimating part 410 estimates aposterior probability sequence P(k) (k=0, 1, 2, . . . , K, k representsa frame number) indicating a probability of sound corresponding to theframe k obtained by dividing the uttered speech s(t) being each phoneme,from the uttered speech s(t) (t=0, 1, 2, . . . , T) using the speechrecognition acoustic model 482 which identifies phonemes from inputsound and outputs the posterior probability sequence P(k) (S410). Theposterior probability sequence P(k) is a vector that is a probability ofsound whose element corresponds to the frame k being each phoneme.Accordingly, a value of each element of the posterior probabilitysequence P(k) is a value between 0 and 1 (both inclusive), and the sumthereof is 1. The posterior probability sequence is estimated using atypical speech recognition model such as a deep neural network (DNN) ora long short-term memory network (LSTM). While the posterior probabilitysequence in FIG. 13 is a monophonic one-state posterior probabilitysequence, a triphonic three-state hybrid deep neural network-hiddenMarkov model (DNN-HMM) which is typically used in speech recognition maybe used.

The second speaking speed estimating part 420 estimates speaking speedmean(r2) of the uttered speech s(t) from the posterior probabilitysequence P(k) (k=0, 1, 2, . . . , K) estimated in S410 using thespeaking speed estimation model 484 and outputs the speaking speedmean(r2) of the uttered speech s(t) (S420). In the speaking speedestimation model 484, a posterior probability sequence of uttered speechis input, and speaking speed of the uttered speech is output. Thespeaking speed estimation model 484 can be configured using a neuralnetwork such as a time series model like an LSTM. Specifically,parameters of the speaking speed estimation model (neural network) arelearned as follows. First, a plurality of speech signals to be used forlearning the speaking speed estimation model are prepared. Next, aposterior probability sequence is obtained for each speech signal usinga method similar to that for the posterior probability sequenceestimating part 410. This posterior probability sequence is used asinput of the speaking speed estimation model to be learned. Further, foreach speech signal, transcription data is created, start time and endtime of respective phonemes constituting the transcription data areobtained, and the speaking speed of the speech signal is obtained usinga method similar to that for the first speaking speed estimating part140. At that time, it is only necessary to use the transcription datainstead of the reading used in the first speaking speed estimating part140. The speaking speed obtained here becomes the correct answer label.Then, the speaking speed is estimated using the posterior probabilitysequence as input and using the speaking speed estimation model, andparameters of the speaking speed estimation model are updated so that anerror between the speaking speed which is the estimation result and thespeaking speed of the correct answer label becomes smaller.

The urgency level estimating part 450 estimates the urgency level of aspeaker of the uttered speech s(t) (t=0, 1, 2, . . . , T) from the meanmean(c) and the variance value var(c) calculated in S120, and thespeaking speed mean(r2) estimated in S420 using the urgency levelestimation model 480 and outputs the urgency level of a speaker of theuttered speech s(t) (S450). In the urgency level estimation model 480, amean and a variance value of a vocal tract feature amount of utteredspeech and speaking speed of the uttered speech are input, and anurgency level of a speaker of the uttered speech is output. A learningmethod of the urgency level estimation model 480 may be similar to thatin the first embodiment.

According to the present invention, it becomes possible to estimate anurgency level of a speaker for free uttered speech, which does notrequire a specific word.

Fifth Embodiment

While, from the first embodiment to the fourth embodiment, the urgencylevel is estimated by combining the vocal tract feature amountstatistical value with one of the feature amounts of the speaking speed,the voice pitch, and the strength of the voice (power level), theurgency level may be estimated by combining the vocal tract featureamount statistical value with two or more feature amounts of thespeaking speed, the voice pitch, and the strength of the voice. Thus, inthe fifth embodiment, a configuration where the urgency level isestimated using combination of the feature amounts used in the first tofourth embodiments will be described. Here, first, a configuration wherethree feature amounts indicated by the speaking speed in the firstembodiment, the voice pitch in the second embodiment and the strength ofthe voice in the third embodiment are used will be described.

An urgency level estimation apparatus 500 will be described below withreference to FIGS. 16 to 17. FIG. 16 is a block diagram illustrating aconfiguration of the urgency level estimation apparatus 500. FIG. 17 isa flowchart illustrating operation of the urgency level estimationapparatus 500. As illustrated in FIG. 16, the urgency level estimationapparatus 500 includes the vocal tract feature amount extracting part110, the vocal tract feature amount statistical value calculating part120, the speech recognizing part 130, the first speaking speedestimating part 140, the F0 information extracting part 210, the F0information statistical value calculating part 220, the power extractingpart 310, the power average adjusting part 320, the power maximum valuecalculating part 330, an urgency level estimating part 550, and arecording part 590. The recording part 590 is a component which recordsinformation necessary for processing of the urgency level estimationapparatus 500 as appropriate.

The urgency level estimation apparatus 500 reads an urgency levelestimation model 580 (not illustrated) and executes processing. It isassumed that the urgency level estimation model 580 is recorded in therecording part 590 in advance.

The urgency level estimation apparatus 500 estimates the urgency levelof a speaker of the uttered speech s(t) from the uttered speech s(t)(t=0, 1, 2, . . . , T, t represents a sample number) and outputs theurgency level of a speaker of the uttered speech s(t).

Operation of the urgency level estimation apparatus 500 will bedescribed with reference to FIG. 17. Processing from S110 to S330 is thesame as that from the first embodiment to the third embodiment.Therefore, processing in S550 will be described below.

The urgency level estimating part 550 estimates the urgency level of aspeaker of the uttered speech s(t) (t=0, 1, 2, . . . , T) from the meanmean(c) and the variance value var(c) calculated in S120, the speakingspeed mean(r) estimated in S140, the difference medave(f) calculated inS220 and the power maximum value max(p) calculated in S330 using theurgency level estimation model 580 and outputs the urgency level of aspeaker of the uttered speech s(t) (S550). In the urgency levelestimation model 580, a mean and a variance value of a vocal tractfeature amount of uttered speech, speaking speed of the uttered speech,a difference between an average value and a median value of F0information of the uttered speech, and a maximum value of adjusted powerof the uttered speech are input, and an urgency level of a speaker ofthe uttered speech is output. A learning method of the urgency levelestimation model 580 may be similar to that in the first embodiment.

While, in the urgency level estimation apparatus 500, the speaking speedin the first embodiment is used, the speaking speed in the fourthembodiment may be used instead of the speaking speed in the firstembodiment. Here, a configuration where three feature amounts indicatingthe speaking speed in the fourth embodiment, the voice pitch in thesecond embodiment, and the strength of the voice in the third embodimentare used will be described.

An urgency level estimation apparatus 501 will be described below withreference to FIGS. 18 to 19. FIG. 18 is a block diagram illustrating aconfiguration of the urgency level estimation apparatus 501. FIG. 19 isa flowchart illustrating operation of the urgency level estimationapparatus 501. As illustrated in FIG. 18, the urgency level estimationapparatus 501 includes the vocal tract feature amount extracting part110, the vocal tract feature amount statistical value calculating part120, the posterior probability sequence estimating part 410, the secondspeaking speed estimating part 420, and the F0 information extractingpart 210, the F0 information statistical value calculating part 220, thepower extracting part 310, the power average adjusting part 320, thepower maximum value calculating part 330, an urgency level estimatingpart 551, and a recording part 590. The recording part 590 is acomponent which records information necessary for processing of theurgency level estimation apparatus 501 as appropriate.

The urgency level estimation apparatus 501 reads an urgency levelestimation model 581 (not illustrated), a speech recognition acousticmodel 482 (not illustrated), and a speaking speed estimation model 484(not illustrated), and executes processing. It is assumed that theurgency level estimation model 581, the speech recognition acousticmodel 482, and the speaking speed estimation model 484 are recorded inthe recording part 590 in advance.

The urgency level estimation apparatus 501 estimates the urgency levelof a speaker of the uttered speech s(t) from the uttered speech s(t)(t=0, 1, 2, . . . , T, t represents a sample number), and outputs theurgency level of a speaker of the uttered speech s(t).

Operation of the urgency level estimation apparatus 501 will bedescribed with reference to FIG. 19. Processing from S110 to S330 is thesame as that from the second embodiment to the fourth embodiment. Thus,processing in S551 will be described below.

The urgency level estimating part 551 estimates the urgency level of aspeaker of the uttered speech s(t) (t=0, 1, 2, . . . , T) from the meanmean(c) and the variance value var(c) calculated in S120, the speakingspeed mean(r2) estimated in S420, the difference medave(f) calculated inS220, and the power maximum value max(p) calculated in S330 using theurgency level estimation model 581, and outputs the urgency level of aspeaker of the uttered speech s(t) (S551). In the urgency levelestimation model 581, a mean and a variance value of a vocal tractfeature amount of uttered speech, speaking speed of the uttered speech,a difference between an average value and a median value of F0information of the uttered speech, and a maximum value of adjusted powerof the uttered speech are input, and an urgency level of a speaker ofthe uttered speech speaker is output. A learning method of the urgencylevel estimation model 581 may be similar to that in the firstembodiment.

While, in the urgency level estimation apparatus 500 and the urgencylevel estimation apparatus 501, in addition to the vocal tract featureamount statistical value, the urgency level is estimated using all threefeature amounts indicating the speaking speed, the voice pitch, and thestrength of the voice, it is not always necessary to use all of thethree feature amounts. The urgency level may be estimated using twofeature amounts among the three feature amounts. In this case, it isonly necessary that the urgency level estimation apparatus includescomponents required for calculating the feature amount to be used forestimation, an urgency level estimating part, and a recording part amongthe components included in the urgency level estimation apparatus 500and the urgency level estimation apparatus 501.

An urgency level estimation apparatus 502 which is an example of such aconfiguration will be described below with reference to FIGS. 20 to 21.FIG. 20 is a block diagram illustrating a configuration of the urgencylevel estimation apparatus 502. FIG. 21 is a flowchart illustratingoperation of the urgency level estimation apparatus 502. As illustratedin FIG. 20, the urgency level estimation apparatus 502 includes afeature amount extracting part 510, an urgency level estimating part552, and a recording part 590. The recording part 590 is a componentwhich records information necessary for processing of the urgency levelestimation apparatus 502 as appropriate.

The urgency level estimation apparatus 502 estimates the urgency levelof a speaker of the uttered speech s(t) from the uttered speech s(t)(t=0, 1, 2, . . . , T, t represents a sample number), and outputs theurgency level of a speaker of the uttered speech s(t).

Operation of the urgency level estimation apparatus 502 will bedescribed with reference to FIG. 21. The feature amount extracting part510 receives the uttered speech s(t) (t=0, 1, 2, . . . , T) as input,and extracts and outputs the feature amount of the uttered speech s(t)(S510). Here, the feature amount includes at least one of a featureindicating the speaking speed of the uttered speech, a featureindicating the voice pitch of the uttered speech, and a featureindicating the power level of the uttered speech. The feature indicatingthe speaking speed of the uttered speech is, for example, the speakingspeed in the first embodiment or the speaking speed in the fourthembodiment, the feature indicating the voice pitch of the uttered speechis, for example, the voice pitch in the second embodiment, and thefeature indicating the power level of the uttered speech is, forexample, the strength of the voice (power level) in the thirdembodiment. In a case where the feature amount includes, for example,the speaking speed in the first embodiment as a feature, the featureamount extracting part 510 preferably includes the speech recognizingpart 130 and the first speaking speed estimating part 140, while, in acase where the feature amount includes the strength of the voice in thethird embodiment, the feature amount extracting part 510 preferablyincludes the F0 information extracting part 210, the power extractingpart 310, the power average adjusting part 320, and the power maximumvalue calculating part 330.

The urgency level estimating part 552 estimates the urgency level of aspeaker of the uttered speech s(t) (t=0, 1, 2, . . . , T) from thefeature amount extracted in S510 based on a relationship between afeature amount extracted from uttered speech and an urgency level of aspeaker of the uttered speech, the relationship being determined inadvance, and outputs the urgency level of a speaker of the utteredspeech s(t) (S552). The relationship between a feature amount and anurgency level is given by, for example, an urgency level estimationmodel in which a feature amount extracted from uttered speech is input,and an urgency level of a speaker of the uttered speech is output.Further, the relationship has the following properties.

(1) In a case where the feature amount includes a feature indicating thespeaking speed of the uttered speech, the urgency level is more likelyto be estimated as higher in a case where the feature indicating thespeaking speed corresponds to faster speaking speed, than in a casewhere the feature indicating the speaking speed corresponds to slowerspeaking speed.(2) In a case where the feature amount includes a feature indicating thevoice pitch of the uttered speech, the urgency level is more likely tobe estimated as higher in a case where the feature indicating the voicepitch corresponds to higher voice pitch than in a case where the featureindicating the voice pitch corresponds to lower voice pitch.(3) In a case where the feature amount includes a feature indicating thepower level of the uttered speech, the urgency level is more likely tobe estimated as higher in a case where the feature indicating the powerlevel corresponds to greater power than in a case where the featureindicating the power level corresponds to smaller power.

According to the present invention, it becomes possible to estimate anurgency level of a speaker for free uttered speech, which does notrequire a specific word.

[Supplementary Notes]

The apparatus according to the present invention includes, for example,as single hardware entity, an input part to which a keyboard, or thelike, can be connected, an output part to which a liquid crystaldisplay, or the like, can be connected, and a communication part towhich a communication apparatus (for example, a communication cable)capable of performing communication with outside of the hardware entitycan be connected, a central processing unit (CPU, which may include acache memory, a register, or the like), a RAM or ROM which is a memory,an external storage apparatus which is a hard disk, and a bus whichconnects the input part, the output part, the communication part, theCPU, the RAM, the ROM, and the external storage apparatus so as to beenable exchange of data. Further, as necessary, the hardware entity maybe provided with an apparatus (drive) which can perform read and writein and from a recording medium such as a CD-ROM. Physical entity havingsuch hardware resources includes a general-purpose computer, or thelike.

In the external storage apparatus of the hardware entity, programsnecessary for realizing the above functions and data necessary forprocessing of the programs are stored (not limited to the externalstorage apparatus, and, for example, the programs may be stored in a ROMwhich is a read-only storage apparatus). Further, data, or the like,obtained through the processing of these programs is stored in a RAM oran external storage apparatus as appropriate.

In the hardware entity, each program stored in the external storageapparatus (or the ROM, or the like) and data necessary for processing ofeach program are read into a memory as necessary, and areinterpreted/executed by the CPU as appropriate. As a result, the CPUrealizes a predetermined function (respective components expressed asthe above-described, part, means, or the like).

The present invention is not limited to the above-described embodiments,and can be modified as appropriate without departing from the spirit ofthe present invention. In addition, the processing described in theabove embodiments may be executed not only in time series in accordancewith the order of description but also in parallel or individually inaccordance with the processing capability of the apparatus whichexecutes the processing or as necessary.

As described above, in a case where the processing functions in thehardware entity (the apparatus of the present invention) described inthe above embodiments are realized by a computer, the processing contentof the functions that the hardware entity should have is described by aprogram. Then, by executing this program on a computer, the processingfunctions of the above hardware entity are realized on the computer.

The program describing this processing content can be recorded on acomputer-readable recording medium. As the computer-readable recordingmedium, for example, any recording medium such as a magnetic recordingapparatus, an optical disk, a magneto-optical recording medium, and asemiconductor memory may be used. Specifically, for example, as themagnetic recording apparatus, a hard disk apparatus, a flexible disk, amagnetic tape or the like, can be used, and as the optical disk, a DVD(Digital Versatile Disc), a DVD-RAM (Random Access Memory), a CD-ROM(Compact Disc Read Only) Memory), a CD-R (Recordable)/RW (ReWritable),or the like, can be used, as the magneto-optical recording medium, an MO(Magneto-Optical disc), or the like, can be used, and, as thesemiconductor memory, an EEP-ROM (Electronically Erasable andProgrammable-Read Only Memory), or the like, be used.

Further, the program is distributed by, for example, selling,transferring, or lending a portable recording medium such as a DVD or aCD-ROM in which the program is recorded. Still further, a configurationmay be employed where the program is distributed by storing the programin a storage apparatus of a server computer and transferring the programfrom the server computer to other computers via a network.

A computer which executes such a program first stores, for example, theprogram recorded on a portable recording medium or the programtransferred from a server computer in the own storage apparatus once.When executing the processing, the computer reads the program stored inthe own storage apparatus and executes the processing in accordance withthe read program. Further, as another execution form of this program,the computer may directly read the program from a portable recordingmedium and execute processing in accordance with the program, or,further, may sequentially execute the processing in accordance with thereceived program every time the program is transferred from the servercomputer to the computer. Further, a configuration may be employed wherethe above-described processing is executed using so-called applicationservice provider (ASP) service which realizes processing functions onlythrough instruction of execution of the program and acquisition of theresult without the program being transferred from the server computer tothe computer. Note that the program in the present embodiment includesinformation which is used for processing by an electronic computer andwhich is equivalent to the program (data, or the like, which is not adirect command to the computer but has property that defines theprocessing of the computer).

While, in this embodiment, the hardware entity is configured by causinga predetermined program to be executed on a computer, at least a part ofthese kinds of processing content may be realized by hardware.

The above description of the embodiments of the present invention hasbeen presented for purposes of illustration and description. There is nointention to be exhaustive and there is no intention to limit theinvention to the exact disclosed form. Modifications and variations arepossible from the above teachings. The embodiments are chosen andexpressed in order to provide the best illustration of the principle ofthe present invention, and to enable those skilled in the art to adaptthe present invention in various embodiments so as to be suitable forconsidered practical use, and make it possible to utilize the presentinvention while various modifications are made. All such modificationsand variations are within the scope of the present invention as definedby the appended claims, which are construed in accordance with thebreadth that is provided impartially, legally and fairly.

What is claimed is:
 1. An urgency level estimation apparatus comprising:a feature amount extracting part configured to extract a feature amountof an utterance from tittered speech; and an urgency level estimatingpart configured to estimate an urgency level of a speaker of the utteredspeech from the feature amount based on a relationship between a featureamount extracted from uttered speech and an urgency level of a speakerof the uttered speech, the relationship being determined in advance,wherein the feature amount includes at least one of a feature indicatingspeaking speed of the uttered speech, a feature indicating voice pitchof the uttered speech, and a feature indicating a power level of theuttered speech.
 2. The urgency level estimation apparatus according toclaim 1, wherein the relationship is provided as an urgency levelestimation model learned so that a feature amount extracted from utteredspeech is input and an urgency level of a speaker of the uttered speechis output.
 3. The urgency level estimation apparatus according to claim1, wherein the relationship is such that in a case where the featureamount includes a feature indicating speaking speed of the utteredspeech, an urgency level is more likely to be estimated as higher in acase where the feature indicating the speaking speed corresponds tofaster speaking speed than in a case where the feature indicating thespeaking speed corresponds to slower speaking speed, in a case where thefeature amount includes a feature indicating voice pitch of the utteredspeech, an urgency level is more likely to be estimated as higher in acase where the feature indicating the voice pitch corresponds to highervoice pitch than in a case where the feature indicating the voice pitchcorresponds to lower voice pitch, and in a case where the feature amountincludes a feature indicating a power level of the uttered speech, anurgency level is more likely to be estimated as higher in a case wherethe feature indicating the power level corresponds to greater power thanin a case where the feature indicating the power level corresponds tosmaller power.
 4. An urgency level estimation apparatus comprising: avocal tract feature amount extracting part configured to extract a vocaltract feature amount for each frame obtained by dividing uttered speech,from the uttered speech; a vocal tract feature amount statistical valuecalculating part configured to calculate a mean and a variance valuefrom the vocal tract feature amount as vocal tract feature amountstatistical values of the uttered speech; a speech recognizing partconfigured to generate a set of reading, an utterance start time and anutterance end time for each utterance section included in the utteredspeech, from the uttered speech; a first speaking speed estimating partconfigured to estimate speaking speed of the uttered speech from the setof the reading, the utterance start time and the utterance end time; andan urgency level estimating part configured to estimate an urgency levelof a speaker of the uttered speech from the mean, the variance value andthe speaking speed using an urgency level estimation model learned sothat a mean and a variance value of a vocal tract feature amount ofuttered speech, and speaking speed of the uttered speech are input, andan urgency level of a speaker of the uttered speech is output.
 5. Anurgency level estimation apparatus comprising: a vocal tract featureamount extracting part configured to extract a vocal tract featureamount for each frame obtained by dividing uttered speech, from theuttered speech; a vocal tract feature amount statistical valuecalculating part configured to calculate a mean and a variance valuefrom the vocal tract feature amount as vocal tract feature amountstatistical values of the uttered speech; an F0 information extractingpart configured to extract F0 information for each frame obtained bydividing the uttered speech, from the uttered speech; an F0 informationstatistical value calculating part configured to calculate a differencebetween an average value and a median value of the F0 information, fromthe F0 information; and an urgency level estimating part configured toestimate an urgency level of a speaker of the uttered speech from themean, the variance value and the difference using an urgency levelestimation model learned so that a mean and a variance value of a vocaltract feature amount of uttered speech, and a difference between anaverage value and a median value of F0 information of the uttered speechare input, and an urgency level of a speaker of the uttered speech isoutput.
 6. An urgency level estimation apparatus comprising: a vocaltract feature amount extracting part configured to extract a vocal tractfeature amount for each frame obtained by dividing uttered speech, fromthe uttered speech; a vocal tract feature amount statistical valuecalculating part configured to calculate a mean and a variance valuefrom the vocal tract feature amount as vocal tract feature amountstatistical values of the uttered speech; an F0 information extractingpart configured to extract F0 information for each frame obtained bydividing the uttered speech, from the uttered speech; a power extractingpart configured to extract power for each frame obtained by dividing theuttered speech, from the uttered speech; a power average adjusting partconfigured to calculate adjusted power adjusted using power average fromthe F0 information and the power; a power maximum value calculating partconfigured to calculate a power maximum value which is a maximum valueof the adjusted power, from the adjusted power; and an urgency levelestimating part configured to estimate an urgency level of a speaker ofthe uttered speech from the mean, the variance value and the powermaximum value using an urgency level estimation model learned so that amean and a variance value of a vocal tract feature amount of utteredspeech, a maximum value of adjusted power of the uttered speech areinput, and an urgency level of a speaker of the uttered speech isoutput.
 7. An urgency level estimation apparatus comprising: a vocaltract feature amount extracting part configured to extract a vocal tractfeature amount for each frame obtained by dividing uttered speech, fromthe uttered speech; a vocal tract feature amount statistical valuecalculating part configured to calculate a mean and a variance valuefrom the vocal tract feature amount as vocal tract feature amountstatistical values of the uttered speech; a posterior probabilitysequence estimating part configured to estimate a posterior probabilitysequence indicating a probability of sound corresponding to a frameobtained by dividing the uttered speech, the sound being each phoneme,from the uttered speech, using a speech recognition acoustic model foridentifying a phoneme from input sound; a second speaking speedestimating part configured to estimate speaking speed of the utteredspeech from the posterior probability sequence using a speaking speedestimation model learned so that a posterior probability sequence ofuttered speech is input, and speaking speed of the uttered speech isoutput; and an urgency level estimating part configured to estimate anurgency level of a speaker of the uttered speech from the mean, thevariance value and the speaking speed using an urgency level estimationmodel learned so that a mean and a variance value of a vocal tractfeature amount of uttered speech, and speaking speed of the utteredspeech are input, and an urgency level of a speaker of the utteredspeech is output.
 8. An urgency level estimation method comprising: afeature amount extracting step of an urgency level estimation apparatusextracting a feature amount of an utterance from uttered speech; and anurgency level estimation step of the urgency level estimation apparatusestimating an urgency level of a speaker of the uttered speech from thefeature amount based on a relationship between a feature amountextracted from uttered speech and an urgency level of a speaker of theuttered speech, the relationship being determined in advance, whereinthe feature amount includes at least one of a feature indicatingspeaking speed of the uttered speech, a feature indicating voice pitchof the uttered speech and a feature indicating a power level of theuttered speech.
 9. A program for causing a computer to function as theurgency level estimation apparatus according to any one of claims 1 to7.